--- title: "Fuli Feng — author profile and papers — Elamur Research" description: "Fuli Feng, University of Science and Technology of China: 11 papers on Elamur Research (2021–2026), h-index 48 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/fuli-feng/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/fuli-feng/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Fuli Feng # Fuli Feng Fuli Feng has 11 papers in our library, in 2021–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Fengbin Zhu](https://md.research.elamur.ai/free/en/authors/fengbin-zhu/), [Wenjie Wang](https://md.research.elamur.ai/free/en/authors/wenjie-wang/), [Wentao Shi](https://md.research.elamur.ai/free/en/authors/wentao-shi/). 11 papers in our library · 2021–2026 [OpenAlex](https://openalex.org/A5051925942) · [ORCID](https://orcid.org/0000-0002-5828-9842) · [Wikidata](https://www.wikidata.org/wiki/Q130800202) · [**h-index 48**](https://openalex.org/authors/A5051925942) · [330 works](https://openalex.org/works?filter=author.id:A5051925942) · [9,093 citations](https://openalex.org/works?filter=author.id:A5051925942&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 11 works in our library](https://md.research.elamur.ai/free/en/authors/fuli-feng/#papers) · [🔗 Cited 448 times in our library](https://md.research.elamur.ai/free/en/authors/fuli-feng/cited-by/) · [6 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #3889](https://md.research.elamur.ai/free/en/paper-stars/page/39/#star-3889) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Fuli+Feng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Fuli+Feng) · [dblp](https://dblp.org/search?q=Fuli+Feng) · [ACL Anthology](https://aclanthology.org/search/?q=Fuli+Feng) ## Affiliations [🎓**University of Science and Technology of China** Hefei · China 2022–2026 · 225 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) · [🎓**National University of Singapore** Singapore · Singapore 2017–2026 · 68 papers*current*](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/) · [🏢**Alibaba Group** Hangzhou · China 2025–2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/companies/alibaba-group/) · [🎓**Tsinghua University** Beijing · China 2018–2021 · 2 papers](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) · [🎓**Beihang University** Beijing · China 2016–2017 · 2 papers](https://md.research.elamur.ai/free/en/universities/beihang-university/) · [🔬**State Key Laboratory of Software Development Environment** Beijing · China 2016–2017 · 2 papers](https://md.research.elamur.ai/free/en/labs/state-key-laboratory-of-software-development-environment/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 11 The field of science this work belongs to (OpenAlex fields).[Computer Science · 11 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 9 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 11 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 9 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Information Systems · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Computational Theory and Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computational-theory-and-mathematics/) · [Experimental and Cognitive Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Software · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/software/) 🧭 Topics 5 Research subjects and languages the work is about.[Evaluation & benchmarks · 2 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Language models & pretraining · 4 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 3 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) ## Works by year ### 2020s [**2026** 6 papers](https://md.research.elamur.ai/free/en/authors/fuli-feng/year/2026/) · [**2025** 4 papers](https://md.research.elamur.ai/free/en/authors/fuli-feng/year/2025/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/authors/fuli-feng/year/2021/) ## Papers The most cited first (OpenAlex and our library). 1. **[TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/)** *(2021 · 397 citations)* Most question answering research handles text, knowledge bases or tables separately. Real documents such as corporate financial reports mix numeric tables with prose that explains or adds to them. This paper introduces TAT-QA, a benchmark built from such reports. Each context pairs one table with at least two related… 2. **[Personalized Generation In Large Model Era: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2025-personalized-generation-large-model-era/)** *(2025 · 24 citations)* Work on generating content tailored to individual users has grown separately in NLP, computer vision and information retrieval. 3. **[Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/deng-2025-unveiling-language-specific-features-large/)** *(2025 · 18 citations)* The paper asks how large language models represent individual languages internally. Earlier work relied on single neurons, which suffer from superposition, or on logit-lens readouts, which are unreliable outside the final layers. The authors instead use pretrained Sparse Autoencoders (SAEs), which split… 4. **[Latent Inter-User Difference Modeling for LLM Personalization](https://md.research.elamur.ai/free/en/papers/qiu-2025-latent-inter-user-difference-modeling/)** *(2025 · 12 citations)* Most LLM personalization conditions generation on a user's own history. However, what makes someone distinctive is how they deviate from other people. An earlier method, DPL, asked the LLM to compare users in natural language. That comparison is hard to steer and fills the prompt with other users' raw text. 5. **[MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation](https://md.research.elamur.ai/free/en/papers/li-2026-mtr-bench-comprehensive-benchmark-multi/)** *(2026 · 9 citations)* Most reasoning benchmarks for LLMs test single-turn problems. Real problem solving is often interactive: a solver asks questions, gets feedback and revises its plan. Earlier multi-turn efforts such as MT-Bench and GameArena either focus on dialogue coherence or depend on costly human involvement. 6. **[Examining False Positives under Inference Scaling for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-examining-false-positives-under-inference/)** *(2025 · 9 citations)* Most math benchmarks for language models award credit by matching the final answer, usually through heuristic rules, and never check the derivation. 7. **[PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-perm-psychology-grounded-empathetic-reward/)** *(2026 · 8 citations)* LLMs used in emotionally sensitive settings often give generic or overly solution-focused replies instead of real emotional support. 8. **[Controllable LLM Reasoning via Sparse Autoencoder-Based Steering](https://md.research.elamur.ai/free/en/papers/fang-2026-controllable-llm-reasoning-sparse-autoencoder/)** *(2026 · 3 citations)* Large Reasoning Models (LRMs) choose their own reasoning moves, such as re-checking or backtracking, and these choices can lead to wasted effort or wrong answers. 9. **[TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards](https://md.research.elamur.ai/free/en/papers/xiong-2026-trojail-trajectory-level-optimization-multi/)** *(2026 · 3 citations)* The paper addresses training automated attackers that break the safety guardrails of black-box LLMs over several dialogue turns. 10. **[Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search](https://md.research.elamur.ai/free/en/papers/shi-2026-efficient-multi-agent-system-training/)** *(2026 · 2 citations)* Jointly optimizing LLM-based multi-agent systems (MAS) is difficult, partly because each agent's contribution to the outcome is hard to measure. 11. **[AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation](https://md.research.elamur.ai/free/en/papers/shi-2026-aj-bench-benchmarking-agent-as/)** *(2026)* As reinforcement learning is scaled up for LLM-based agents, checking whether an agent actually did its job becomes a bottleneck. ## All works 17 works of theirs in our library, the most cited first; 11 with a page here, the others link their original until we summarise them. 1. **[TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation ↗](https://doi.org/10.1145/3604915.3608857) arXiv:2305.00447 · no summary yet**Keqin Bao; Jizhi Zhang; Yang Zhang; Wenjie Wang; Fuli Feng;, Xiangnan He*2023 · 531 citations* 2. **[Causal Intervention for Leveraging Popularity Bias in Recommendation ↗](https://doi.org/10.1145/3404835.3462875) International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yet**Yang Zhang et al.*2021 · 437 citations* 3. **[Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution ↗](https://doi.org/10.24963/ijcai.2017/536) International Joint Conference on Artificial Intelligence · no summary yet**Guangyao Shen et al.*2017 · 409 citations* 4. **[TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fengbin Zhu, Wenqiang Lei, Youcheng Huang, et al.*2021 · 397 citations* 5. **[Deconfounded Video Moment Retrieval with Causal Intervention ↗](https://doi.org/10.1145/3404835.3462823) International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yet**Xun Yang et al.*2021 · 206 citations* 6. **[Counterfactual Inference for Text Classification Debiasing ↗](https://aclanthology.org/2021.acl-long.422/) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yet**Chen Qian et al.*2021 · 106 citations* 7. **[How to Retrain Recommender System? ↗](https://doi.org/10.1145/3397271.3401167) International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yet**Yang Zhang et al.*2020 · 71 citations* 8. **[Personalized Generation In Large Model Era: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2025-personalized-generation-large-model-era/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiyan Xu, Jinghao Zhang, Alireza Salemi, et al.*2025 · 24 citations* 9. **[Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/deng-2025-unveiling-language-specific-features-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boyi Deng, Yu Wan, Baosong Yang, et al.*2025 · 18 citations* 10. **[Latent Inter-User Difference Modeling for LLM Personalization](https://md.research.elamur.ai/free/en/papers/qiu-2025-latent-inter-user-difference-modeling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, et al.*2025 · 12 citations* 11. **[MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation](https://md.research.elamur.ai/free/en/papers/li-2026-mtr-bench-comprehensive-benchmark-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyuan Li, Keqin Bao, Yubo Ma, et al.*2026 · 9 citations* 12. **[Examining False Positives under Inference Scaling for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-examining-false-positives-under-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yu Wang, Nan Yang, Liang Wang, et al.*2025 · 9 citations* 13. **[PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-perm-psychology-grounded-empathetic-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengbing Wang, Wuqiang Zheng, Yang Zhang, et al.*2026 · 8 citations* 14. **[Controllable LLM Reasoning via Sparse Autoencoder-Based Steering](https://md.research.elamur.ai/free/en/papers/fang-2026-controllable-llm-reasoning-sparse-autoencoder/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yi Fang, Wenjie Wang, Mingfeng Xue, et al.*2026 · 3 citations* 15. **[TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards](https://md.research.elamur.ai/free/en/papers/xiong-2026-trojail-trajectory-level-optimization-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiqiao Xiong, Ouxiang Li, Zhuo Liu, et al.*2026 · 3 citations* 16. **[Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search](https://md.research.elamur.ai/free/en/papers/shi-2026-efficient-multi-agent-system-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wentao Shi, Zichun Yu, Fuli Feng, et al.*2026 · 2 citations* 17. **[AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation](https://md.research.elamur.ai/free/en/papers/shi-2026-aj-bench-benchmarking-agent-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wentao Shi, Yu Wang, Yuyang Zhao, et al.*2026* ## Co-authors here 12 of 33 co-authors with a page, the most papers together first. - [Fengbin Zhu](https://md.research.elamur.ai/free/en/authors/fengbin-zhu/) ×4 - [Wenjie Wang](https://md.research.elamur.ai/free/en/authors/wenjie-wang/) ×4 - [Wentao Shi](https://md.research.elamur.ai/free/en/authors/wentao-shi/) ×3 - [Xiangnan He](https://md.research.elamur.ai/free/en/authors/xiangnan-he/) ×3 - [Boyi Deng](https://md.research.elamur.ai/free/en/authors/boyi-deng/) ×2 - [Dayiheng Liu](https://md.research.elamur.ai/free/en/authors/dayiheng-liu/) ×2 - [Moxin Li](https://md.research.elamur.ai/free/en/authors/moxin-li/) ×2 - [Tat-Seng Chua](https://md.research.elamur.ai/free/en/authors/tat-seng-chua/) ×2 - [Yang Zhang](https://md.research.elamur.ai/free/en/authors/yang-zhang/) ×2 - [Yu Wang](https://md.research.elamur.ai/free/en/authors/yu-wang/) ×2 - [Alireza Salemi](https://md.research.elamur.ai/free/en/authors/alireza-salemi/) - [Baosong Yang](https://md.research.elamur.ai/free/en/authors/baosong-yang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×7: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×4, [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) ×2, [ACL 2021](https://md.research.elamur.ai/free/en/venues/acl/2021/) - International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) ×3 - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ×2: [EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/) ×2 - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×2: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×2 - International Joint Conference on Artificial Intelligence - Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) ## Cited by Papers of our library cite their works 448 times (of the 149,439 papers whose bibliographies we read); [93 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/fuli-feng/cited-by/). Updated 1 November 2026